Purdue University Graduate Certificate Program in Veterinary Homeland Security
Bibliographic record
Abstract
Our nation lacks a critical mass of professionals trained to prevent and respond to food- and animal-related emergencies. Training veterinarians provides an immediate means of addressing this shortage of experts. Achievement of critical mass to effectively address animal-related emergencies is expedited by concurrent training of professionals and graduate students in related areas. Purdue University offers a Web-based Graduate Certificate in Veterinary Homeland Security to address this special area of need. The program is a collaborative effort among the Purdue University School of Veterinary Medicine, the Purdue Homeland Security Institute, the Indiana State Board of Animal Health, the Indiana State Police, and others with the overall goal of increasing capacity and preparedness to manage animal-related emergencies. Individuals with expertise in veterinary medicine, public health, animal science, or homeland security are encouraged to participate. The Web-based system allows courses to be delivered efficiently and effectively around the world and allows participants to continue their graduate education while maintaining full-time jobs. Participants enhance their understanding of natural and intentional threats to animal health, strengthen their skills in managing animal-health emergencies, and develop problem-solving expertise to become effective members of animal emergency response teams and of their communities. Students receive graduate credit from Purdue University that can be used toward the certificate and toward an advanced graduate degree. Currently, 70 participants from 28 states; Washington, DC; Singapore; and Bermuda are enrolled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.423 | 0.146 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".